# An Extended Newton-type Algorithm for ℓ2-Regularized Sparse Logistic Regression and Its Efficiency for Classifying Large-scale Datasets

arXiv: https://arxiv.org/abs/1901.02768 · HTML: https://mlsanity.com/paper/1901.02768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with ℓ0-constraints](https://mlsanity.com/paper/2309.09239/) (2023)

## Abstract (arXiv)

Sparse logistic regression, as an effective tool of classification, has been developed tremendously in recent two decades, from its origination the ℓ1-regularized version to the sparsity constrained models. This paper is carried out on the sparsity constrained logistic regression by the Newton method. We begin with establishing its first-order optimality condition associated with a τ-stationary point. This point can be equivalently interpreted as a system of equations which is then efficiently solved by the Newton method. The method has a considerably low computational complexity and enjoys global and quadratic convergence properties. Numerical experiments on random and real data demonstrate its superior performance when against seven state-of-the-art solvers.